[BioC] Significant p-values disappear in limma

[Sean Davis]

Have you run p.adjust on the p-values from limma?  I think limma uses 
p.adjust directly, so you can check (for fun) to see that you get the 
same results.  Power to detect differentially-expressed genes is NOT 
just a function of the number of arrays, but also of the experiment.  
(In other words, just because Gordon shows finding differentially 
expressed genes in the swirl experiment with four arrays doesn't mean 
that all experiments with only four arrays have enough power to detect 
small differences.)  You could increase the number of experiments or 
you could just accept that, while not statistically certain of your 
gene list, it probably represents a good ordering and then proceed with 
your confirmatory experiments based on the ordering.  Did you consider 
using SAM (in siggenes), just to see if that gets you more (not likely, 
but...)?

On Jan 5, 2005, at 8:07 AM, michael watson ((IAH-C)) wrote:

> Hi Sean
>
> Unfortunately this one is out of my control (as usual), but I have much
> smaller p-values with 4 arrays before, and even with 3 arrays.  Also
> note that in one of my four-array experiments, EVERY single p-value was
> 0.9999963 after adjusting for the fdr - that's over 4600 spots, all 
> with
> the same p-value.
>
> Finally, note that the SWIRL dataset has only 4 arrays and limma
> produces many, many p-values <= 0.05.
>
> So, although I admit 4 arrays is far from ideal in terms of power,
> something is nagging me that that's not it, and it certainly wouldn't
> explain why over 4600 spots all have the same adjusted p-value - would
> it?

You are positing a bug in limma?  Like I mentioned, try running the 
p-values from limma through p.adjust.  Alternatively, try using the 
qvalue package, just to see what you get.  But, yes, I have seen the 
majority (I don't think all) of my genes have the same large p-value.

Sean